The client had proven the technology. The harder question was whether it could become a business. Specifically: how do you scale autonomous ride-hail commercially without assuming the capital burden of owning a fleet?
Engaged to build the business value case — a financial model and partnership framework that would answer whether the asset-light model could work, what the economics looked like for both the company and its partners, and which path to market made sense. This is the define layer: before anyone builds or fixes anything, someone has to determine whether the model is viable at all.
At 2,500 vehicles, is unit economics viable enough to attract fleet operators and capital without the company owning assets?
Franchise model (partner-owned fleet) vs. corporate model (operator-owned fleet) — different risk, return, and scalability profiles for each party.
Fleet operators, rental companies, PE firms, OEM dealerships — each with different motivations. The case had to prove ROI for the partner, not just the client.
The client's equity as a technology company was strong. Its equity as a ride-hail provider was nascent. Consumer adoption was the variable that would make or break unit economics.
Charging costs were ~80% of OpEx — making energy infrastructure the primary lever for partner economics, not fleet size. Ride volume, not vehicle count, determined returns.
AV regulation evolving at federal and local levels simultaneously. Any commercial model had to account for constraints that would shift — and position the company to influence, not just react.
"The technology was proven. The business model wasn't. The job was to quantify whether autonomous ride-hail could attract partners, split economics fairly, and generate returns — at a scale nobody had actually done before."
A financial model built from first principles around 2,500 vehicles — 20 rides/day, $21 average fare, 80% utilization. Two revenue split scenarios modeled (20/80 and 70/30 client/Fleet Operator), projecting NOI for both parties and isolating the variables that most affected partner returns.
A partnership framework assessing franchise vs. corporate structures across six partner archetypes — fleet operators, rental companies, PE firms, OEM dealerships, robo-taxi fleets, and consumer brands. Recommendation: franchise-first. Lower client CapEx, faster market entry, consumer brand partnerships as the adoption accelerant.
Figures are from the financial model produced in the engagement. These are modeled outputs, not operational results — the value of the work is the analysis, not the outcome.
| Finding | What It Shows | Figure |
|---|---|---|
|
Client NOI · asset-light model
20/80 split · 2,500 vehicles · fleet operator bears CapEx
|
At the asset-light split, the client retains the technology premium while offloading capital risk — and still projects $61M NOI at initial scale. | $61M NOI |
|
Fleet Operator NOI · same scenario
80% revenue share · operator bears CapEx
|
Partner economics are compelling enough to attract capital without the client subsidizing the deal — the asset-light model works for both sides. | $163M NOI |
|
Fleet operator 3-year ROI
Franchise model · stable across 500–2,500 vehicles
|
Returns are consistent across fleet sizes — ride volume is the primary driver, not scale. A 500-vehicle operator has a viable business case. | 70% 3-yr ROI |
|
Client 3-year ROI · corporate model
70/30 split · client absorbs all OpEx
|
Even absorbing full OpEx including charging, the client's returns create headroom to subsidize partner adoption — giving the company commercial flexibility at scale. | 386% 3-yr ROI |
|
Primary OpEx lever
Charging infrastructure · energy costs
|
Charging is ~80% of total OpEx. Energy cost management is the single biggest lever for improving partner economics and structuring competitive royalties. | ~80% of OpEx |
|
Recommendation
Franchise vs. corporate · path to market
|
Franchise-first: lower CapEx requirement, faster entry, existing fleet operators as the most viable first partners. Consumer brand partnerships as the trust accelerant. | Franchise-first |
| Scale modeled | 2,500 vehicles |
This is the define layer — the work that happens before anyone builds or fixes anything. The technology existed. The question was whether the commercial model could support it. Answering that required building a financial model with no precedent, structuring a partnership framework for a market that didn't exist yet, and producing analysis credible enough to sit in front of a highly technical executive audience.
Not every engagement starts with something broken. Some start with something unproven. The diagnostic muscle is the same either way — figure out what's actually true before anyone commits capital to a direction.